General Discussion of the Background
In recent years, there has been an ever increasing emphasis on improving the rational use of medical treatments to help reduce health care costs and enhance the quality of medical care. Previous efforts to manage health care, however, have often included retrospective approaches in which individual patient files were laboriously reviewed to determine the appropriateness of medical care a patient received. Such retrospective analyses can be assisted by categorization of diseases into Diagnosis Related Groups (DRGs), or International Classification of Disease (ICD) codes, which exhaustively classify illnesses and provide a statistical estimate of the acceptable cost of treating a particular medical condition.
Several automated systems have previously been proposed to help improve the effectiveness and reduce the cost of medical care. U.S. Pat. No. 5,583,758, for example, discloses a computer system in which a proposed treatment for a disease is entered into a computer, and the proposed treatment is compared to an approved treatment. If the proposed treatment differs from the approved treatment, the user is notified that specialist review is required to implement the proposed treatment.
In U.S. Pat. No. 5,018,067 a computer system is disclosed which calculates the severity of a patient's illness (based on the same criteria used for a DRG classification), and compares patient outcomes for patients having illnesses of similar acuity. The cost performance of a physician or hospital can then be determined by comparing the health care provider's actual costs incurred to an expected expense for treating a disease in the DRG classification. The computer system can also detect a changing diagnosis, which may indicate an improper initial diagnosis or treatment. This information provides a tool for monitoring physician and hospital performance.
U.S. Pat. No. 5,557,514 discloses a computer system for analyzing historical medical provider billings to establish a normative utilization profile. An individual provider's utilization profile is compared with a normative profile, to identify medical providers who provide treatments that do not fall within statistically established treatment patterns or profiles.
Efforts have also been made to provide laboratory information in a format that increases the efficiency of physicians who need to interpret the data to recommend a treatment. For example, U.S. Pat. No. 5,551,022 describes a computer implemented nodal tree structure for storing, consolidating and displaying microbiological laboratory results. This nodal structure allows the results of multiple microbiological tests to be consolidated and displayed in a matrix that facilitates the selection of appropriate antibiotic treatment.
Although these systems have used computers in an attempt to collect information about health care delivery, and provide information to a physician or other health care provider, these systems all assume that the ideal treatment is relatively fixed. This concept has resulted in the establishment of "formularies" at many hospitals, which provide a rigid list of available drugs (usually based on the cost of the drugs). This formulary approach is contrary to the realities of medicine, in which treatments evolve (increasingly quickly) as the pace of medical research advances. The fixed nature of recommended treatments also ignores the fact that diseases in different geographic locales are actually changed by the treatments selected, and that treatments are often more successful with certain ethnic and racial groups than with others.
An example of this problem is that bacteria develop resistance to antibiotic treatment. Hence antimicrobial treatment guidelines embedded in a computer program or hospital formulary can actually worsen a medical outcome by forcing physicians to overtreat with a particular antibiotic, against which microbial resistance is therefore developed. Moreover, once resistance develops, the recommended treatments suggested by the system can be dangerous, because they may have become ineffective. In addition, continuing to treat with the recommended antibiotic will only increase the development of resistance against that drug, and make it more difficult to treat subsequent patients who are infected with that microbial pathogen.
A related problem is that recommendations for pharmaceutical treatment are typically provided in national guidelines each year, based on statistical information gathered throughout the country. This approach has ignored the local nature of many diseases. Antibiotic resistance, for example, develops locally and spreads nationally, hence national guidelines may be inappropriate for a particular community. The long periods of time that elapse between the issuance of the guidelines also permits patterns of antibiotic resistance to become well established. This delay also means that clinicians who follow the national guidelines may be empirically selecting less than optimal initial antibiotic treatment for a disease pending the outcome of laboratory culture and sensitivity studies. Selection of an initial inappropriate empirical treatment can increase the length of stay of a patient in a hospital, which in turn increases health care expenditures. Hence existing approaches to treating infectious diseases can increase the cost of health care by perpetuating treatments that may no longer be optimally effective.
Yet another problem with drug treatment is that national recommendations for therapy may be inappropriate for certain subpopulations of patients. A population served by a particular hospital may have a large percentage of elderly patients, or patients of a particular race or ethnic group, who do not react well to a particular medication. The classic example of such an intolerance is the toxicity of primaquine (an anti-malarial agent) in blacks, Sardinians, Sephardic Jews, Greeks and Iranians. At the present time there is no mechanism for identifying optimal pharmaceutical treatments for such subpopulations of patients, and providing the optimal pharmaceutical intervention. There are few systems in place for identifying drugs that work best with subpopulations within a restricted geographic area, such as a state, city, or a particular hospital or health care system.
Drug studies are frequently performed to analyze the effectiveness of drugs, but such studies are often limited to test subjects of a particular race or gender. Such studies ignore the diversity of subpopulations of patients throughout the country, and sometimes cause inadequate or inappropriate treatments to be provided. This problem has become particularly acute now that hospitals and other health service centers are providing strict guidelines about the drugs that can be prescribed for patients who have particular diseases. Many of these recommendations are based solely on the purchase price of the drug, while ignoring other cost factors such as side-effects and medication errors that can complicate treatment and increase its actual cost.